Detecting pairwise correlations in high-density recordings: open science in action

Detecting pairwise correlations in high-density recordings: open science in action

Stephen J Eglen                  Cambridge Computational Biology Institute
https://sje30.github.io          University of Cambridge
sje30@cam.ac.uk                  @StephenEglen

Slides: http://bit.ly/eglen-comp

CNS 2017 Workshop Neuronal population recordings
These slides are available under a creative common CC-BY license.

http://bit.ly/eglen-comp

Acknowledgements

Catherine Cutts, Tom Edinburgh, Gerrit Hilgen, Evelyne Sernagor.

BBSRC, EPSRC, Wellcome Trust, software Sustainibility Institute.

What are retinal waves?

Correlation spike trains

Retinal waves: Wong et al (1993) (fig 8, 9)

How to interpret figures?

1. three regions: [0,1], 1, [1, ∞]
2. lower firing rates -> higher CI

What method should we use?

Phase 1: finding a short list

34 measures in literature + 1 from us => 35.

Six necessary properties:

1. Symmetric
2. Robust to variations in firing rate
3. Robust to amount of data
4. Bounded [-1, +1]
5. Robust to variations in bin width (Δt)
6. Anticorrelation: The measure should discriminate between no correlation and anticorrelation.

Dependence on firing rate

Phase 2: Desirable properties

Desirable properties:

1. D1: Ignore periods when both neurons are inactive.
2. D2: minimal assumptions on structure.
3. D3: aside from Δt, minimise number of parameters

Four methods:

1. Kerschensteiner and Wong correlation (D1, D2)
2. Spike count correlation (D2, D3)
3. Kruskal et al. binless correlation measure (D1?, D2, D3)
4. Tiling coefficient (D1, D2, D3)

Tiling measure

Evidence

Blankenship et al (2011)

Correlations decay with age

Inference methods

Biorxiv paper.

Correlogram methods

Extending across time with tau.

Looking for needles in haystacks.

Competitions: burst analysis

Reproducible research

To do this work we needed access to data. We have released this (Eglen et al 2014, Gigascience).

Then and now